Please use this identifier to cite or link to this item: https://doi.org/10.1021/tx049652h
Title: Prediction of genotoxicity of chemical compounds by statistical learning methods
Authors: Li, H. 
Ung, C.Y. 
Yap, C.W. 
Xue, Y. 
Li, Z.R. 
Cao, Z.W.
Chen, Y.Z. 
Issue Date: Jun-2005
Citation: Li, H., Ung, C.Y., Yap, C.W., Xue, Y., Li, Z.R., Cao, Z.W., Chen, Y.Z. (2005-06). Prediction of genotoxicity of chemical compounds by statistical learning methods. Chemical Research in Toxicology 18 (6) : 1071-1080. ScholarBank@NUS Repository. https://doi.org/10.1021/tx049652h
Abstract: Various toxicological profiles, such as genotoxic potential, need to be studied in drug discovery processes and submitted to the drug regulatory authorities for drug safety evaluation. As part of the effort for developing low cost and efficient adverse drug reaction testing tools, several statistical learning methods have been used for developing genotoxicity prediction systems with an accuracy of up to 73.8% for genotoxic (GT+) and 92.8% for nongenotoxic (GT-) agents. These systems have been developed and tested by using less than 400 known GT+ and GT-agents, which is significantly less in number and diversity than the 860 GT+ and GT- agents known at present. There is a need to examine if a similar level of accuracy can be achieved for the more diverse set of molecules and to evaluate other statistical learning methods not yet applied to genotoxicity prediction. This work is intended for testing several statistical learning methods by using 860 GT+ and GT- agents, which include support vector machines (SVM), probabilistic neural network (PNN), k-nearest neighbor (A-NN), and C4.5 decision tree (DT). A feature selection method, recursive feature elimination, is used for selecting molecular descriptors relevant to genotoxicity study. The overall accuracies of SVM, k-NN, and PNN are comparable to and those of DT lower than the results from earlier studies, with SVM giving the highest accuracies of 77.8% for GT+ and 92.7% for GT- agents. Our study suggests that statistical learning methods, particularly SVM, k-NN, and PNN, are useful for facilitating the prediction of genotoxic potential of a diverse set of molecules. © 2005 American Chemical Society.
Source Title: Chemical Research in Toxicology
URI: http://scholarbank.nus.edu.sg/handle/10635/114375
ISSN: 0893228X
DOI: 10.1021/tx049652h
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